Flood frequency analysis at ungauged sites using artificial neural networks in canonical correlation analysis physiographic space
Bibliographic record
Abstract
Models based on canonical correlation analysis (CCA) and artificial neural networks (ANNs) are developed to obtain improved flood quantile estimates at ungauged sites. CCA is used to form a canonical physiographic space using the site characteristics from gauged sites. Then ANN models are applied to identify the functional relationships between flood quantiles and the physiographic variables in the CCA space. Two ANN models, the single ANN model and the ensemble ANN model, are developed. The proposed approaches are applied to 151 catchments in the province of Quebec, Canada. Two evaluation procedures, the jackknife validation procedure and the split sample validation procedure, are used to evaluate the performance of the proposed models. Results of the proposed models are compared with the original CCA model, the canonical kriging model, and the original ANN models. The results indicate that the CCA‐based ANN models provide superior estimation than the original ANN models. The ANN ensemble approaches provide better generalization ability than the single ANN models. The CCA‐based ensemble ANN model has the best performance among all models in terms of prediction accuracy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".